Cell Segmentation in Microscopy
Imagery Using a Bag of Local
Bayesian Classifiers
Zhaozheng Yin
RI/CMU, Fall 2009
Motivation
• Accurate segmentation is challenging
Segmentation using a single threshold yields poor results:
Segmentation using a singe global Bayesian classifier also generates bad results:
Our Solution
• A bag of local Bayesian classifiers:
• Local Bayesian classifiers (experts) are learned
from clustered training image patches.
• Any new pixel to be classified is assigned a
posterior probability about how likely it is a
cell or background pixel based on the mixtureof-experts model.
System Overview
Train and combine a bag of local Bayesian classifiers:
Using the Bayes’ rule on each local Bayesian classifier, we have :
where:
is the feature around pixel x, for example, intensity, gradient etc.
represents pixel class (Cell or Background )
is the weight dependent on the input (different from boosting)
A new input pixel is classified by Maximum a Posteriori (MAP):
Training (get
1.
)
Spectral clustering on local histograms
(a) Compute local histograms around N sample pixels
(b) Compute a pair-wise similarity matrix among the N histograms.
(c) Group the N histograms into K clusters.
Training (get
)
2. Train local Bayesian classifiers
(d) Achieve local histogram clusters from the spectral clustering
(e) Obtain corresponding clustered image patches
(f) Train local Bayesian classifiers from the clustered image patches
Classification
• First , we calculate a local histogram
around , and then
compute the similarity between
and every histogram cluster,
, where
represents the histogram of cluster .
• The weighting function on classifier
is defined as
• We combine the local Bayesian classifiers as
• Pixel is classified by
Classifier 1
h=5
win
size
h=10
h=15
Classifier 2
Classifier 3
Results
Cyan square: miss detection
Yellow circle: false alarm
Red:
our detection
Green contour:
ground truth
Cyan square: miss detection
Yellow circle: false alarm
Red:
our detection
Green contour:
ground truth
Cyan square: miss detection
Yellow circle: false alarm
Red:
our detection
Green contour:
ground truth
Cyan square: miss detection
Yellow circle: false alarm
Red: our detection
Green contour: ground truth
Cyan square: miss detection
Yellow circle: false alarm
Red:
our detection
Green contour:
ground truth
Input:
Cell posterior probability:
Ground truth labeling:
Bayesian Classifiers on DIC Images
• We use intensity and gradient features on DIC images
10 bin Ix (intensity)
10 bin Gx
(gradient
magnitude)
Cluster
Win sz
h=5
h = 10
h = 20
k=1
k=2
k=3
Conclusion
• We propose a bag of local Bayesian classifier
approach for cell segmentation in microscopy
imagery.
• Our approach is validated on four types of
cells of different appearances captured by
different imaging modalities and device
settings with 92.5% average accuracy.